3D light-sheet microscopy data for SELMA3D 2026 challenge - isolated structures - training subset with annotations

ABSTRACT: This dataset is the training set with annotations of isolated structures for SELMA3D 2026 challenge. The SELMA3D 2026 challenge focuses on self-supervised learning for 3D light-sheet microscopy (LSM) image segmentation. Its objective is to encourage the development of generalizable models capable of serving multiple 3D LSM image segmentation tasks. This dataset contains 3D image patches of different isolated structures including c-Fos labeled brain cells involved in neural activity, cell nuclei, and Alzheimer's disease plaques. Each patch includes corresponding pixel-wise annotations for the structures.

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PROVIDER: S-BIAD2110 | bioimages |

REPOSITORIES: bioimages